Communication between agents needs to be flexible enough to encompass together a variety of different aspects such as, conformance to society protocols, private tactics of the individual agents, strategies that reflect different classes of agent types (or personal attitudes) and adaptability to the particular external circumstances at the time when the communication takes place. In this paper we propose an argument-based framework for representing communication theories of agents that can take into account in a uniform way these different aspects. We show how this approach can be used to realize existing types of dialogue strategies and society protocols in a way that facilitates their modular development and extension to make them more flexible in handling different or special circumstances.

Abstract. When agents communicate, they do not necessarily use the same vocabulary or ontology. For them to interact successfully, they must find correspondences (mappings) between the terms used in their respective ontologies. While many proposals for matching two agent ontologies have been presented in the literature, the resulting alignment may not be satisfactory to both agents, and thus may necessitate additional negotiation to identify a mutually agreeable set of correspondences. We propose an approach for supporting the creation and exchange of different arguments, that support or reject possible correspondences. Each agent can decide, according to its preferences, whether to accept or refuse a candidate correspondence. The proposed framework considers arguments and propositions that are specific to the matching task and are based on the ontology semantics. This argumentation framework relies on a formal argument manipulation schema and on an encoding of the agents ’ preferences between particular kinds of arguments. Whilst the former does not vary between agents, the latter depends on the interests of each agent. Thus, this approach distinguishes clearly between alignment rationales which are valid for all agents and those specific to a particular agent. 1

Negotiation is a fundamental interaction mechanism in multi-agent systems because it allows self-interested agents to come to mutually beneficial agreements and partition resources efficiently and effectively. Now, in many situations, the agents need to negotiate with one another many times and so developing strategies that are effective over repeated interactions is an important challenge. Against this background, a growing body of work has examined the use of Persuasive Negotiation (PN), which involves negotiating using rhetorical arguments (such as threats, rewards, or appeals), in trying to convince an opponent to accept a given offer. Such mechanisms are especially suited to repeated encounters because they allow agents to influence the outcomes of future negotiations, while negotiating a deal in the present one, with the aim of producing results that are beneficial to both parties. To this end, in this paper, we develop a comprehensive PN mechanism for repeated interactions that makes use of rewards that can be asked for or given to. Our mechanism consists of two parts. First, a novel protocol that structures the interaction by capturing the commitments that agents incur when using rewards. Second, a new reward generation algorithm that

Abstract. This paper presents the design of an open architecture for heterogeneous negotiating agents. Both the system level architecture as well as the architecture for negotiating agents are provided. The main contribution of this paper is that it derives a precisely specified interface from these architectures that facilitates an easy integration of heterogeneous agents into the overall negotiation framework. The interface is defined as a set of adapters that allows for various levels of integration of agents into the system architecture. The functionality provided by the system architecture depends on the number of adapters that are implemented and used to connect an agent to this architecture, ranging from functionality to conduct a bilateral negotiation to functionality for computing agent internal performance measures such as the quality of an opponent model. The architecture is used as the basis of a competitive testbed which allows us to study various negotiating agents. The design yields a flexible negotiation framework that facilitates negotiating different domains potentially using different protocols whereas no details of the internal negotiating agent structure are enforced. An application of the framework is illustrated by integrating two agents from the literature. 1

MAS) involves finding an adequate formalization of an agent's knowledge to perform defeasible inferences in a computationally effective way. In the last years, argument-based approaches have proven to be an attractive setting to achieve this goal. Dealing with uncertainty and fuzziness associated with the available knowledge are also common requirements in MAS. Such features, however, are not embedded in most argument-based formalisms. Possibilistic Defeasible Logic Programming (P-DeLP) has recently appeared as an alternative to solve the above problem. P-DeLP is a logic programming language which combines features from argumentation theory and logic programming, incorporating as well the treatment of possibilistic uncertainty and fuzzy knowledge at object-language level. This paper describes how P-DeLP can be applied in the context of formalizing an agent's beliefs and perceptions, along with an argumentative inference procedure to determine which of the agent's beliefs are ultimately accepted (or warranted).

Abstract. Recent years have seen an increasing interest of multiagent system research in employing the theory of argumentation for the development of communication protocols. While significant progress has been made in formalising argument-based communication, (possibly adaptive) agent-level argumentation strategies as a practical integration of rational agent reasoning and inter-agent dialogue have received fairly little attention. In this paper we propose an implementation of a simplified form of argument-based negotiation using a generic social reasoning framework. This framework allows for strategic and adaptive communication towards private goals within the limits of rationality. The feasibility of the approach is illustrated in an agent-based web linkage scenario, showing that its performance is comparable to that of simple proposal-based negotiation while imposing much stricter and much more realistic constraints. 1

Abstract. Communication between agents needs to be flexible enough to encompass together a variety of different aspects such as, conformance to society protocols, private tactics of the individual agents, strategies that reflect different classes of agent types (or personal attitudes) and adaptability to the particular external circumstances at the time when the communication takes place. In this paper we propose an argument-based framework for representing communication theories of agents that can take into account in a uniform way these different aspects. We show how this approach can be used to realize existing types of dialogue strategies and society protocols in a way that facilitates their modular development and extension to make them more flexible in handling different or special circumstances.

Two prevalent approaches to automated negotiation are the application of game-theoretic notions and the argumentation-based angle; these two schemes are frequently at odds. An elegant view of argumentation is Dung’s abstract argumentation theory [2], which cold-shoulders the internal structure of arguments in favor of the entire debate’s global structure. Dung’s theory is elaborated by work in dialectical argumentation theory, which focuses on dialogues between two players. In this paper, we enhance the abovementioned frameworks by considering two-agent settings where each of the agents is identified with a set of arguments. A binary attack relation between arguments is given, as well as (most importantly) a payoff function that assigns real values to every possible valid dialogue. Such game-based argumentation frameworks can be lucidly realized as games in extensive form (marrying, in a sense, the game-theoretic and argumentation-based approaches). We investigate specialized notions of “maximally-defendable ” sets of arguments, and the correlation between the properties of the agents ’ argument sets and the size of the associated game tree. Furthermore, algorithmic issues are considered: we present simplifications and efficient solutions for our argumentation games, and discuss a special case where the representation of the framework is logarithmic in the size of the associated game tree. 1

Abstract. We present a novel logic-based framework to automate multi-issue bilateral negotiation in e-commerce settings. The approach exploits logic as communication language among agents, and optimization techniques in order to find Pareto-efficient agreements. We introduce P(N), a propositional logic extended with concrete domains, which allows one to model relations among issues (both numerical and non-numerical ones) via logical entailment, differently from wellknown approaches that describe issues as uncorrelated. Through P(N) it is possible to represent buyer’s request, seller’s supply and their respective preferences as formulas endowed with a formal semantics, e.g., “if I spend more than 30000 e for a sedan then I want more than a two-years warranty and a GPS system included”. We mix logic and utility theory in order to express preferences in a qualitative and quantitative way. We illustrate the theoretical framework, the logical language, the one-shot negotiation protocol we adopt, and show we are able to compute Pareto-efficient outcomes, using a mediator to solve an optimization problem. We prove the computational adequacy of our method by studying the complexity of the problem of finding Pareto-efficient solutions in our setting. 1

Until now, AI argumentation-based systems have been mainly developed for handling inconsistency. In that explanation-oriented perspective, only one type of argument has been considered. Several argumentation frameworks have then been proposed for generating and evaluating such arguments.